Drought forecasting using data-driven methods and an evolutionary algorithm

Modeling Earth Systems and Environment

Volumn 3, Issue 4, 2017, Pages 1675-1689

  Hosseini Moghari, Seyed Mohammad ^a   Araghinejad, Shahab ^a   Azarnivand, Ali ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

Author keywords

Drought forecasting; Evolutionary algorithm; Multi layer perceptron; Standard precipitation index; Support vector regression

Indexed keywords

ACCURACY ASSESSMENT; ALGORITHM; DROUGHT; PRECIPITATION ASSESSMENT; QUANTITATIVE ANALYSIS; SUPPORT VECTOR MACHINE; WEATHER FORECASTING;

IRAN;

EID: 85084650379     PISSN: 23636203     EISSN: 23636211     Source Type: Journal    
DOI: 10.1007/s40808-017-0385-x     Document Type: Article

References (51)

1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723 DOI: 10.1109/TAC.1974.1100705
2. Almedeij J (2016) Long-term periodic drought modeling. Stochastic Environ Res Risk Assess 30(3):901–910 DOI: 10.1007/s00477-015-1065-x
3. Araghinejad S (2013) Data-driven modeling: using MATLAB® in water resources and environmental engineering. vol 67. Springer Science & Business Media, Berlin
4. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE Congress on Evolutionary Computation, 4661–4667
5. Azarnivand A, Banihabib ME (2016) A multi-level strategic group decision making for understanding and analysis of sustainable watershed planning in response to environmental perplexities. Group Decis Negot. doi:10.1007/s10726-016-9484-8
6. Bacanli UG, Firat M, Dikbas F (2009) Adaptive neuro-fuzzy inference system for drought forecasting. Stoch Environ Res Risk Assess 23(8):1143–1154 DOI: 10.1007/s00477-008-0288-5
7. Barua S, Ng AWM, Perera BJC (2012) Artificial neural network–based drought forecasting using a nonlinear aggregated drought index. J Hydrol Eng 17(12):1408–1413 DOI: 10.1061/(ASCE)HE.1943-5584.0000574
8. Belayneh A, Adamowski J (2013) Drought forecasting using new machine learning methods/Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod. J Water Land Dev 18(9):3–12 DOI: 10.2478/jwld-2013-0001
9. Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, Hoboken
10. Bozorg-Haddad O, Azarnivand A, Hosseini-Moghari SM, Loáiciga HA (2016a) Development of a comparative multiple criteria framework for ranking Pareto optimal solutions of a multiobjective reservoir operation problem. J Irrig Drain Eng 142(7):04016019 DOI: 10.1061/(ASCE)IR.1943-4774.0001028
11. Bozorg-Haddad O, Azarnivand A, Hosseini-Moghari SM, Loáiciga HA (2016b) WASPAS application and evolutionary algorithm benchmarking in optimal reservoir optimization problems. J Water Resour Plann Manage 143(1):04016070 DOI: 10.1061/(ASCE)WR.1943-5452.0000716
12. Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1):113–126 DOI: 10.1016/S0893-6080(03)00169-2
13. Cheval S, Busuioc A, Dumitrescu A, Birsan MV (2014) Spatiotemporal variability of meteorological drought in Romania using the standardized precipitation index SPI. Clim Res 60(3):235–248 DOI: 10.3354/cr01245
14. Chisadza B, Tumbare MJ, Nyabeze WR, Nhapi I (2015) Linkages between local knowledge drought forecasting indicators and scientific drought forecasting parameters in the Limpopo River Basin in Southern Africa. Int J Disaster Risk Reduct 12:226–233 DOI: 10.1016/j.ijdrr.2015.01.007
15. Chitsaz N, Hosseini-Moghari SM (2017) Introduction of new datasets of drought indices based on multivariate methods in semi-arid regions. Hydrol Res. doi:10.2166/nh.2017.254
16. Chitsaz N, Azarnivand A, Araghinejad S (2016) Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique. Hydrol Sci J 61(12):2164–2178 DOI: 10.1080/02626667.2015.1085991
17. Dastorani MT, Afkhami H (2011) Application of artificial neural networks on drought prediction in Yazd (Central Iran). Desert 16(1):39–48
18. Durdu ÖF (2010) Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey. Stoch Environ Res Risk Assess 24(8):1145–1162 DOI: 10.1007/s00477-010-0366-3
19. Edwards DC (1997) Characteristics of 20th century drought in the United States at multiple time scales (No. afit-97-051). Air force inst of tech wright-patterson afb oh
20. Eslamian S (ed) (2014) Handbook of engineering hydrology: modeling, climate change, and variability. CRC Press, Boca Raton DOI: 10.1201/b16766
21. Gorgij AD, Kisi O, Moghaddam AA (2016) Groundwater budget forecasting, using hybrid wavelet-ANN-GP modelling: a case study of Azarshahr Plain, East Azerbaijan, Iran. Hydrol Res. doi:10.2166/nh.2016.202
22. Guttman NB (1999) Accepting the Standardized Precipitation Index: a calculation algorithm. J Am Water Resour Assoc 35(2):311–322 DOI: 10.1111/j.1752-1688.1999.tb03592.x
23. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366 DOI: 10.1016/0893-6080(89)90020-8
24. Javan K, Lialestani MRFH, Nejadhossein M (2015) A comparison of ANN and HSPF models for runoff simulation in Gharehsoo River watershed, Iran. Model Earth Syst Environ 1(4):41 DOI: 10.1007/s40808-015-0042-1
25. Jayanthi H, Husak GJ, Funk C, Magadzire T, Adoum A, Verdin JP (2014) A probabilistic approach to assess agricultural drought risk to maize in Southern Africa and millet in Western Sahel using satellite estimated rainfall. Int J Disaster Risk Reduct 10:490–502 DOI: 10.1016/j.ijdrr.2014.04.002
26. Kan G, Yao C, Li Q, Li Z, Yu Z, Liu Z, Liang K (2015) Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model. Stoch Environ Res Risk Assess 29(5):1345–1370 DOI: 10.1007/s00477-015-1040-6
27. Kisi O, Dailr AH, Cimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450:48–58 DOI: 10.1016/j.jhydrol.2012.05.031
28. McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology, vol 17, no 22, American Meteorological Society, Boston, pp 179–183
29. Mishra AK, Desai VR (2005) Drought forecasting using stochastic models. Stochastic. Environ Res Risk Assess 19(5):326–339 DOI: 10.1007/s00477-005-0238-4
30. Mishra AK, Desai VR (2006) Drought forecasting using feed-forward recursive neural network. Ecol Model 198(1):127–138 DOI: 10.1016/j.ecolmodel.2006.04.017
31. Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1):202–216 DOI: 10.1016/j.jhydrol.2010.07.012
32. Mishra AK, Singh VP (2011) Drought modeling—A review. J Hydrol 403(1):157–175 DOI: 10.1016/j.jhydrol.2011.03.049
33. Mishra AK, Desai VR, Singh VP (2007) Drought forecasting using a hybrid stochastic and neural network model. J Hydrol Eng 12(6):626–638 DOI: 10.1061/(ASCE)1084-0699(2007)12:6(626)
34. Modarres R (2007) Streamflow drought time series forecasting. Stoch Environ Res Risk Assess 21(3):223–233 DOI: 10.1007/s00477-006-0058-1
35. Morid S, Smakhtin V, Bagherzadeh K (2007) Drought forecasting using artificial neural networks and time series of drought indices. Int J Climatol 27(15):2103–2111 DOI: 10.1002/joc.1498
36. Pandey PK, Nyori T, Pandey V (2017) Estimation of reference evapotranspiration using data driven techniques under limited data conditions. Model Earth Syst Environ 1–13
37. Pangapanga PI, Jumbe CB, Kanyanda S, Thangalimodzi L (2012) Unravelling strategic choices towards droughts and floods’ adaptation in Southern Malawi. Int J Disaster Risk Reduct 2:57–66 DOI: 10.1016/j.ijdrr.2012.08.002
38. Partal T, Cigizoglu HK, Kahya E (2015) Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data. Stoch Environ Res Risk Assess 29(5):1317–1329 DOI: 10.1007/s00477-015-1061-1
39. Reed PM, Hadka D, Herman JD, Kasprzyk JR, Kollat JB (2013) Evolutionary multiobjective optimization in water resources: the past, present, and future. Adv Water Resour 51(1):438–456 DOI: 10.1016/j.advwatres.2012.01.005
40. Schwarz G (1978) Estimating the dimension of a model. Annal Stat 6(2):461–464 DOI: 10.1214/aos/1176344136
41. Shamshirband S, Amirmojahedi M, Gocić M, Akib S, Petković D, Piri J, Trajkovic S (2015) Estimation of reference evapotranspiration using neural networks and cuckoo search algorithm. J Irrig Drain Eng 142(2):04015044 DOI: 10.1061/(ASCE)IR.1943-4774.0000949
42. Sharma N, Zakaullah M, Tiwari H, Kumar D (2015) Runoff and sediment yield modeling using ANN and support vector machines: a case study from Nepal watershed. Model Earth Syst Environ 1(3):23 DOI: 10.1007/s40808-015-0027-0
43. Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng 129(3):214–218 DOI: 10.1061/(ASCE)0733-9437(2003)129:3(214)
44. Taormina R, Chau KW (2015) Neural network river forecasting with multi-objective fully informed particle swarm optimization. J Hydroinformatics 17(1):99–113 DOI: 10.2166/hydro.2014.116
45. Tay FE, Cao L 2001 Application of support vector machines in financial time series forecasting. Omega 29(4):309–317 DOI: 10.1016/S0305-0483(01)00026-3
46. Udmale PD, Ichikawa Y, Manandhar S, Ishidaira H, Kiem AS, Shaowei N, Panda SN (2015) How did the 2012 drought affect rural livelihoods in vulnerable areas? Empirical evidence from India. Int J Disaster Risk Reduct 13:454–469 DOI: 10.1016/j.ijdrr.2015.08.002
47. Vapnik V (1995) The nature of statistical learning theory. Springer Verlag, New York DOI: 10.1007/978-1-4757-2440-0
48. Wagh VM, Panaskar DB, Muley AA, Mukate SV, Lolage YP, Aamalawar ML (2016) Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India. Model Earth Syst Environ 2(4):196 DOI: 10.1007/s40808-016-0250-3
49. Wilhite DA, Glantz MH (1985) Understanding: the drought phenomenon: the role of definitions. Water Int 10(3):111–120 DOI: 10.1080/02508068508686328
50. Wilhite DA, Rosenberg NJ, Glantz MH (1986) Improving federal response to drought. J Climate Appl Meteorol 25(3):332–342 DOI: 10.1175/1520-0450(1986)025<0332:IFRTD>2.0.CO;2
51. Wu CL, Chau KW, Li YS (2008) River stage prediction based on a distributed support vector regression. J Hydrol 358(1):96–111 DOI: 10.1016/j.jhydrol.2008.05.028